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MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities

, , , , , , and . Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, (October 2022)

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Modeling Recurring Concepts in Single-label and Multi-label Streams.. University of Mainz, Germany, (2020)FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework., , , and . KDD, page 3140-3150. ACM, (2023)A label compression method for online multi-label classification., and . Pattern Recognit. Lett., (2018)Modeling Multi-label Recurrence in Data Streams., and . ICBK, page 9-16. IEEE, (2019)MANDO-HGT: Heterogeneous Graph Transformers for Smart Contract Vulnerability Detection., , , , , , and . MSR, page 334-346. IEEE, (2023)On the Impact of Dataset Size: A Twitter Classification Case Study., , , , and . WI/IAT, page 210-217. ACM, (2021)To Parse or Not to Parse: An Experimental Comparison of RNTNs and CNNs for Sentiment Analysis., , , and . EMSASW@ESWC, volume 1874 of CEUR Workshop Proceedings, CEUR-WS.org, (2017)Inductive and transductive link prediction for criminal network analysis, , , , , , , , and . Journal of Computational Science, (September 2023)MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities, , , , , , and . Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, (October 2022)MANDO-GURU: vulnerability detection for smart contract source code by heterogeneous graph embeddings., , , , , and . ESEC/SIGSOFT FSE, page 1736-1740. ACM, (2022)